Tracking of tumor location for targeted radiation treatment

ABSTRACT

Systems, methods, and apparatuses are provided for targeting diseased tissue with a radiation beam. Functional models can be used to accurately obtain a location of specific tissue using sensors at identifiable locations of the patient&#39;s body. Using the relative distances between the identifiable sensor locations can allow a patient to be in various positions. The functional models can be prepared using accurate pre-treatment scans, which can be taken at various body positions (e.g., rotations and/or translations). The trajectory of the beam can be measured efficiently and accurately using beam sensors attached to a beam assembly, where a model maps the beam sensor locations to a trajectory of the beam. Further, a motion model can use measurements made during treatment to obtain a time-dependent functions of the movement of the specific tissue, the change of an optimal beam trajectory over time, or the change in input commands to a beam positioner.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority from and is a non-provisionalapplication of U.S. Provisional Application No. 61/435,195, entitled“Non-Invasive Tracking of Tumor Location for Targeted RadiationTreatment” filed Jan. 21, 2011, the entire contents of which are hereinincorporated by reference for all purposes.

BACKGROUND

The present invention relates to targeted radiation treatment (alsocalled radiosurgery), and more specifically to techniques of determininga location of a tumor or other diseased tissue for determining atrajectory of a radiation beam.

Radiation beams have been used to kill diseased tissue (e.g. a tumor).However, the radiation beam can also kill healthy tissue. Thus, methodshave been used to determine a location of a tumor so that the radiationbeam can be focused on the tumor. For example, the radiation beam canmove over time to minimize exposure of healthy tissue while stayingfocused on the tumor. An x-ray can be taken at the beginning of thetreatment to identify fiducials (marking objects) that have beensurgically placed on the tumor, thereby providing the location of thetumor. This invasive method is costly and can be dangerous to thepatient.

Some methods restrict a patient to a specified position for the durationof a treatment so that the position of the tumor stays known. Suchrestriction can be quite uncomfortable for the patient, and errors canoccur due to imperfect restriction. Methods can take repeated x-rays ofinternal markers to update the position of the tumor while breathing,but such methods expose the patient to a large amount of radiation viathe numerous x-rays and require the motion to be periodic. Methods canomit the implantation of fiducials by correlating a location of certainbones, which tend not to move during treatment, to the tumor location.But, the patient is still restricted to a particular position, or atleast a particular orientation (e.g. lying flat on one's back). Thesemethods also still suffer from numerous x-rays if the location of thetumor is to be updated.

U.S. patent publication 2008/0212737 omits the numerous x-rays duringtreatment and the implantation of fiducials while still accounting forthe movement due to breathing; however, the patient is still restrictedto certain positions. For example, the patient is restricted to lying onhis/her back on a special table while being held in place. A scan isperformed at different times during the breathing cycle, with each timein the breathing cycle corresponding to a distance in positions ofsensors on the patient's chest compared to a sensor in the specialtable. The scans can then be used to determine a location of the tumorsduring radiation beam treatment, but the location is accurate only whenthe person is in the same exact location as when the scans were taken.Thus, although procedure is non-invasive and limits excessive radiationscans during treatment, the person is still confined and uncomfortableduring treatment. Furthermore, this application only handles smallperiodic motion such as breathing. Different positions of the patientare not allowed.

Additionally, the equipment for creating the radiation beam must beprecisely calibrated so that a control input corresponds to the exactlocation where the tumor is determined to be. The equipment must be madewith very high tolerance so that the control inputs correspond theproper beam placement. Thus, the beam equipment can be very expensive.Additionally, current techniques do not properly handle beam positioningerror.

Therefore it is desirable to have improved systems and methods forproviding targeted radiation treatment that can variously allow apatient freedom of movement without excessive radiation, are easy touse, do not require difficult calibration, are non-invasive, allowmovement beyond simply breathing, and compensate for beam positing errorand other systematic errors in the system.

BRIEF SUMMARY

Some embodiments can provide freedom of movement to a patient undergoingradiation beam treatment. For example, prior to a treatment session, oneor more scans (e.g. MRI or CT) of a patient can be taken to determine alocation of a tumor relative to markers of a patient's body. A mappingmodel can be built from the various relative positions of the markers toobtain an output of the tumor location, which can be done withoutcomplicated image reconstruction during treatment. As the markers(sensors) are attached to the patient's body, the patient is notrestricted to any particular position or orientation. Thus, scans can bemade for different positions of the patient, such as standing up,sitting, and lying down (e.g. on back or side), which can beincorporated in the mapping model. Thus, in one aspect, the patient canchoose a natural position specific to that patient. A mapping model canalso use scans from other patient's with similar body characteristics todecrease the amount of time and scans to build a model for a patient.Also, a pre-treatment image (e.g., using CT or MRI) can be transformedto a treatment coordinate system, and a best-fit process can be used tomap the corrected pre-treatment image onto a treatment image (e.g.,obtained from the sensors during treatment). Thus, a patient does nothave to be fixed to a specific location.

Other embodiments can provide for accurate determination of thetrajectory of a radiation beam, e.g., by obtaining feedback of a beamposition using sensors on a beam assembly. In one aspect, a beamassembly using sensor feedback can be less expensive since the controlinput for positioning of the beam does not need to correspond exactly tothe desired trajectory (such precision instruments are expensive and aretime consuming to calibrate). For example, the control input can bechanged until the sensors indicate that the desired trajectory has beenachieved. Such feedback can also provide and maintain greater accuracyrelative to instruments that rely on a static relationship betweencontrol input and trajectory.

Additionally, embodiments can account for motion of the tissue bycreating a time-dependent model based on data taken during treatment. Inthis manner, the patient can be allowed in other ways besidespredetermined types of movement, such as breathing. Additionally,embodiment can address errors in beam positioning. Errors may arise fromdelays in detecting positions of markers, calculating tumor locationsfrom the marker positions, determining an optimal beam trajectory, andmoving the radiation beam to an optimal location. The errors can behandled in various ways. For example, a time offset for computing a nextposition of tissue can be used to adjust for the delays. Such a timeoffset can be computed in an open-loop fashion or with a feedback ofmeasured errors. As another example, a model can be used to predict atime-dependent function of the input commands, where the function can beadjusted based on feedback in errors of the actual beam trajectory overtime.

Other embodiments are directed to systems, apparatuses, and computerreadable media associated with methods described herein.

Reference to the remaining portions of the specification, including thedrawings and claims, will realize other features and advantages of thepresent invention. Further features and advantages of the presentinvention, as well as the structure and operation of various embodimentsof the present invention, are described in detail below with respect tothe accompanying drawings. In the drawings, like reference numbers canindicate identical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a diagram of a patient 100 undergoing radiation beamtreatment according to embodiments of the present invention.

FIG. 1B shows a diagram of a patient 100 undergoing an imaging scanaccording to embodiments of the present invention.

FIG. 2A is a flowchart of a method 200 for determining a location ofdiseased tissue (e.g. a tumor) according to embodiments of the presentinvention.

FIG. 2B shows a treatment enclosure 280 according to embodiments of thepresent invention.

FIGS. 3A shows a patient 300 with markers 320 according to embodimentsof the present invention.

FIG. 3B shows the relative coordinates of markers 320 compared to tumor310 according to embodiments of the present invention.

FIGS. 3C and 3D show a reference object 370 that may be used during ascan of a patient according to embodiments of the present invention.

FIG. 4 shows a diagram of a patient undergoing treatment according toembodiments of the present invention.

FIG. 5 shows an origin sensor 535 from which the relative positions ofthe sensors are determined according to embodiments of the presentinvention.

FIGS. 6A-6C shows the relative coordinates of the sensors being used todetermine the location of the tumor location according to embodiments ofthe present invention.

FIG. 7 shows a system for positioning a trajectory of a radiation beamfrom a beam assembly 720 according to embodiments of the presentinvention.

FIG. 8A shows an intermittent error in beam positioning due to movementof a tumor 810 according to embodiments of the present invention. FIG.8B shows an example of a prediction of a location of a tumor betweensampling times according to embodiments of the present invention.

FIG. 9A shows a constant error in beam positioning due to movement of atumor 910 according to embodiments of the present invention. FIG. 9Bshows an example of a prediction of a location of a tumor betweensampling times where the prediction accounts for a delay betweensampling and positioning of a beam according to embodiments of thepresent invention.

FIG. 10 is a flowchart illustrating a method 1000 for tracking motion oftissue and determining an optimal beam position based on the motionaccording to embodiments of the present invention.

FIG. 11 is a flowchart illustrating a method 1100 for determining anoptimal beam position based on feedback error according to embodimentsof the present invention.

FIG. 12 shows a block diagram of an example computer system 1200 usablewith system and methods according to embodiments of the presentinvention.

DETAILED DESCRIPTION I. Introduction

FIG. 1 shows a diagram of a patient 100 undergoing radiation beamtreatment according to embodiments of the present invention. The patient100 is shown as laying down on his/her back, but other body positionsand orientations are allowed. A beam assembly 120 is shown in aparticular orientation to provide a radiation beam 130 that is focusedon a tumor 110 inside patient 100. Beam assembly 120 can be connected toa movement mechanism that allows beam assembly 120 to be moved. Forexample, beam assembly 120 can be part of a robotic mechanism that sitson a floor of a room, is attached to a wall, or hangs from a ceiling.

In one embodiment, beam assembly 120 may be moved during treatment sothat healthy tissue is not irradiated for too long. For example, if thebeam always had the same trajectory, the tissue above the tumor 110would continuously be exposed to radiation. If the beam assembly movedwhile staying focused, the same healthy tissue would not be continuouslyexposed.

In order to stay focused on the tumor 110, the location of tumor 110needs to be known. This is true regardless of whether beam assembly 120moves during treatment or not. Embodiments can provide non-invasivetechniques for determining a location of tumor 110 while allowing apatient to be in different physical positions. For example, someembodiments perform an imaging scan prior to treatment.

FIG. 1B shows a diagram of the patient 100 undergoing an imaging scanaccording to embodiments of the present invention. FIG. 1B is shown onthe left of the page to illustrate that this imaging is performed beforethe treatment. The scan can be in a different room and be done on adifferent patient visit than the treatment. In another embodiment, theroom for scanning can also be used for treatment, and the patient visitcan be on the same day. In the embodiment shown, a computed tomography(CT) scan is used, but other suitable scans may be used, such asmagnetic resonance imaging (MRI). These accurate scan(s) of the patientand tumor can provide coordinates of the tumor relative to markersattached to the patient's body. Such a method is now described.

II. Using Mapping Model

FIG. 2A is a flowchart of a method 200 for determining a location ofdiseased tissue (e.g. a tumor) according to embodiments of the presentinvention. In one aspect, method 200 uses imaging scans (such as MRI orCT) to develop a 3D model of the location of the tumor for differentphysical positions of the patient. The physical positions of the patientcan be defined using markers at a surface of the patient's body. Sensorsat these marker positions (or at a defined position relative to themarker positions) can be used to determine the physical position of thepatient's body during treatment.

In step 210, a plurality of markers are placed at a surface of thepatient's body. The markers may be any object (e.g. ink, sensor, pellet,tag, etc.) whose position can be detected during a scan of the patient.In one embodiment, the mechanism for detecting the marker position canbe the same mechanism for the scan of the tissue (e.g. MRI), and thusthe coordinates of the markers are in the same reference frame as thecoordinates of the various tissue that is obtained in the scan. Forexample, the markers can show up in the image scan. In anotherembodiment, some other mechanism (e.g. an RF signal or an opticalsignal) can be used to determine the coordinates of the markers, and thetwo coordinate systems of the scanned tissue and the markers can bemerged such that relative positions between the markers and the varioustissues can be determined.

FIG. 3A shows a patient 300 with markers 320 at a surface of thepatient's body. The markers 320 can be on the front or back of thepatient, head, appendages, or at any other surface of the patient'sbody. In some embodiments, only one or two markers may be sufficient. Inother embodiments, a larger number of markers may be used. The markerscan be attached or otherwise put on the patient via any suitable manner,such as adhesives, mechanical attachment to or at a surface of the skin,or just as a layer that binds to the skin. The different makers 320 canbe distinguished based on the known locations where the markers wereplaced, by a unique signature that identifies the marker in a scan, orin any other suitable manner.

The markers can also be identifiable features of a person's body, suchas an elbow, a nose (even just the tip), a nipple, belly button,shoulder, etc. The markers can be identified using cameras, such as atypical camera operating with visible light, or other ranges may be usedin addition or instead, e.g., infra-red and/or ultraviolet. Thus,artificial markers do not have to be placed on the body, since naturalbody markers can be used. Natural and artificial markers can be used incombination.

In addition to the identifiable markers on a surface of a body, internalmarkers could be used, as long as the locations of the markers could beaccurately and efficiently identified during treatment. For example,x-rays or ultrasound could detect a position of a particular locationon, for example, the spine or femur for the leg, or even soft tissue(which could include the tumor). However, such methods could presentdifficulties in resolving precise locations of a bone. Such internalmarkers may provide supplementary information to the positions of theexternal markers, e.g., in order to refine the mapping model duringtreatment. For imaging soft tissue during treatment, the accuracy may below, but some rough values (e.g. located by with larger margins ofaccuracy than a sensor location) can be obtained. A distance or distancerange of the tissue from the sensors (which can be internal markers) canthen be compared to the mapping model to ensure that the mapping modelis accurate, and to possibly update the mapping model (e.g. using a bestfit algorithm). The best fit can determine the maximum likelihood of thetumor location based on the additional scan (i.e. the scan duringtreatment), which can provide a location of clear internal markers (suchas bones), external markers, and a fuzzy location of any one or moresoft tissue (which can include the tumor or healthy tissue), along withinformation from the more accurate pre-treatment scans. In one aspect,the update may be performed when the mapping model is shown to beinaccurate from the best fit model that incorporates

In step 220, the patient is scanned to determine the positions of tissue(diseased or healthy) relative to the markers. For example, the absolutepositions of the tissue and the markers can be determined in aparticular reference frame. Scans can choose any coordinate system (e.g.Euclidean, spherical, etc) with an arbitrary origin. The absolutecoordinates of the tissue and the markers can then be determined in thiscoordinate system. For example, the coordinates of tumor 310 and ofmarkers 320 can be determined.

In some embodiments, multiple scans can be performed at different bodypositions, as is described in more detail below. Each scan can provide adifferent set of relative coordinates. A set of relative coordinates fora particular scan can provide a multi-dimensional point defining alocation of tumor 310 for a particular physical position of the patientduring a scan. The various physical positions can include sittingupright, laying down, standing, and sitting in a reclined position.

In step 230, the relative coordinates from the one or more scans areused to determine a functional model that maps marker locations to atumor location for a physical position of the patient. In oneembodiment, the functional model receives information for the relativecoordinates of the markers as input and provides an output of thelocation of the tumor. The input locations are not restricted to therelative locations in the one or more scans that were performed, but canbe other relative locations that correspond to other physical positionsof the patient. For example, intermediate positions may be betweenlaying on a side and laying on a back. The functional model can becreated in various ways. In one embodiment, the functional model canhave constraints, e.g., some physical positions (relative coordinates)may be rejected if they appear to deviate drastically from normalphysical positions (e.g., a rejection of a contortionist body position).

FIG. 3B shows the relative coordinates of markers 320 compared to tumor310. In various embodiments, the relative coordinates can be to a centerof mass of the tumor, center of volume of the tumor, or each to aparticular point on a surface of the tumor (e.g. the closest point onthe surface relative to a particular marker). The relative coordinatesof the markers to each other can be determined from the relative to thetumor, or from the absolute coordinates of the markers themselves.

Other objects besides the markers may also be placed at the surface ofthe patient's body. FIGS. 3C and 3D show a reference object 370 that maybe used during a scan of a patient according to embodiments of thepresent invention. The reference object 370 is shown as a triangle butany shape may be used. The reference object is of a known dimension andits position can be determined in the same manner as the markers. Withthe known dimension, the distances between the markers can be verified,calibrated, and/or corrected. For example, a length on the image can beobtained from the object, and this length can then be used to obtain theproper scale in the image, which would provide the accurate lengthbetween two markers. In one aspect, the relative coordinates would bescaled based on the reference scale provide by the reference object toprovide more accurate relative coordinates and relative vectors betweenthe markers.

In other embodiments, a distance of the markers can be measured by handor some separate mechanism to provide the reference distance. Thus, thereference object could include the markers, e.g., a specific set ofmarkers whose pair-wise distances can be measured. The reference objectcould also include other marks whose relative distance to the tumor isnot used, but whose positions are measured to provide a reference scalefor the image. The reference object can be made up of any number ofpairs of such marks to provide a reference scale in numerous directions,which can account for variable distortion along different directions.

In some embodiments, more than one reference object may be used. Thedifferent reference objects can be used to provide a reference scale inmultiple directions. For example, a reference object could be placed onthe patients side, thereby providing a reference scale for depth, whichcan provide corrections that are different than the corrections obtainedfrom a reference object on the patient's torso (which may just provide areference scale for width and height).

In step 240, sensors are attached to the patient's body and positions ofsensors are determined. For example, the sensors can be placed at asurface of the patient's body, or possibly surgically implanted withinthe patient's body for some embodiments. The positions of the sensorscan be determined with respect to a reference point having a knownspatial relationship to a radiation beam assembly (e.g. beam assembly120), which is configured to provide a radiation beam. The sensors maybe wireless (e.g. optical, infrared, Wi-Fi, etc.), or be wired. In otherembodiments, natural markers (such as facial features, or even bonefeatures, as is described herein) may be used as the sensors, and thusnew artificial sensors do not need to be attached.

The position of the sensors can be determined (e.g. sampled) at periodicintervals to track movement of the patient from one position to anotherposition. In one embodiment, the sensors can be at a same location asmarkers, or simply be the markers. In another embodiment, a sensor canbe at a location that is at a predetermined offset from the location ofa marker.

In one embodiment, the sensors are placed on a surface of the patient.In another embodiment, the sensors are placed slightly below a surfaceof the patient's body. Both locations correspond to being at a surface.Similarly, the markers can be placed at a surface of the patient. Thesensors can be placed at a same location as the markers. In oneimplementation, this can be accomplished with a semi-permanent mark(which can be the marker) so that the location of the sensor can beknown. The semi-permanent marker can be made to last for the duration ofthe treatment, which can be anywhere from a day to one week or a month,or longer.

FIG. 4 shows a diagram of a patient undergoing treatment according toembodiments of the present invention. Wireless sensors 430 are at asurface of the patient's body. The positions of the wireless sensors 430can be determined from detectors 440, which may be connected to acomputer system. The wireless sensors can implement any suitabletechnology, such as zigby, wi-fi, Bluetooth, optical/laser technologyetc. For optical sensors, their position can be determined from areflection of radiation (e.g. visible light) off of the sensor. Anoptical sensor can even be a particular body feature, e.g., asidentified by a recognition algorithm that analyzes a picture of thepatient, which may be performed using two or more digital cameras. Thepixel positions of the optical sensor on the images from the cameras canbe correlated to a particular 3-dimensional spatial coordinate, e.g.,using triangulation. The correlation (mapping) may be performed using abest fit algorithm. The mapping can be calibrated with known objects(which may be of known shape) at known distances, e.g., within atreatment enclosure. Such known objects could be flashing or include anactive sensor, which may be used to independently confirm or calculatethe location of the known object. Thus, the sensor can be the markerused in step 210, including internal markers.

The detectors 440 can be used to triangulate the positions of thesensors relative to an origin of the room. For example, the detectors440 can each be at a known position, and thus at known positionsrelative to each other. The signals from each detector can then becompared to determine the position of a sensor. Again, any coordinatesystem can be chosen, and the origin is arbitrary. In oneimplementation, the detectors 440 can be calibrated by measuring therelative distances of sensors that have a known spatial relationship.

Embodiments can use various methods to determine the positions of thesensors, such as GPS positioning technology, optical imaging of thesensor locations, and passive or active wireless communication devices.In one embodiment, the sensors could receive signals and then transmitlocation. In another embodiment, the sensors could transmit signals anddetectors can determine the location. In one implementation, the sensorseach have a unique signal so that the sensors can be distinguished.

In step 250, positions of the sensors relative to each other aredetermined from the determined positions of the sensors in the treatmentroom. In one embodiment, the relative coordinates can be defined withrespect to one of the sensors, which can be taken as the origin in therelative coordinate system. Thus, in one embodiment with N sensors in a3-dimensional environment, N−1 relative positions (each with 3coordinates) can be determined, where the positions are relative to theorigin sensor. In this case, 3*(N−1) relative coordinates would bedetermined. For example, since there are N−1 sensors besides the originsensor, there will be N−1 relative positions, and 3*(N−1) relativecoordinates.

FIG. 5 shows an origin sensor 535 from which the relative positions ofthe sensors are determined according to embodiments of the presentinvention. Note that the relative marker positions can also be measuredfrom an origin marker, and thus the sets of relative positions ofsensors can be used as input to the functional model. Beam assembly 520and detectors 540 can function as described herein.

In step 260, the relative coordinates are input into the mapping modelto determine the tumor location. In one embodiment, the tumor locationcan be defined relative to an origin sensor. A position relative to theorigin sensor can then be translated to an absolute position in thetreatment enclosure (e.g., the room or smaller containment unit that ismeant to house the body), given the location of the origin sensor. Forexample, the position of the origin sensor can be with respect to areference point (e.g., an origin of the treatment enclosure). Thus,absolute position can be obtained, where the absolute position is withrespect to the reference point.

FIGS. 6A-6C shows the relative coordinates of the sensors being used todetermine the location of the tumor according to embodiments of thepresent invention. FIG. 6A shows the relative coordinates of the sensors630 at a particular instant in time. The arrows show a vector definingthe relative coordinates. Only two relative coordinates are shown,purely for illustration purposes.

The relative coordinates are fed into the mapping function to providethe location of the tumor 610 as defined from the origin sensor. FIG. 6Bshows the resulting relative coordinate vector providing the tumorlocation relative to the origin sensor 635. FIG. 6C shows the positionthe tumor location relative to an origin 660 of the treatment enclosure(e.g. a room). As shown, the tumor location is obtained as a combinationof position of the origin sensor and the relative coordinate of thetumor. Thus, in some embodiments, the tumor location can be determinedjust from the sensors, and without prior knowledge of the location ofthe tumor in the room or relative to a stationary object in the room.

In step 270, the position and orientation of the beam assembly isdetermined based on the location of tumor. The coordinate position andorientation (e.g. angular orientation) of the beam assembly can definethe trajectory a radiation beam being emitted from the beam assembly.Since the position of the beam assembly with respect to the referencepoint is known, and the position of the tumor with respect to thereference point is known, one can determine the position and orientationof the beam assembly that directs the radiation beam to be focused onthe tumor. Other factors, such as the location of healthy tissue, can beused to select an optimal position and orientation, such that the beamfollows an optimal path. In one embodiment, the wireless sensors (whichmay be optical sensors) can be used to determine the position andorientation of beam assembly so that the beam stays focused on thetumor, even while the beam assembly is moving so as not to burn healthytissue.

In one embodiment, locations of healthy tissue are also determined. Forexample, it may be desired to provide none or minimal radiation tocertain organs, e.g., the heart. The locations of these particularorgans that can receive none or minimal radiation may be used todetermine the proper beam trajectory.

The radiation treatment may be provided in any suitable treatmentenclosure, such as a room or a self-contained module. For instance, acapsule (e.g., cylindrical or rectangular) can include the beam assemblyand a mechanism to move the beam assembly. For example, the treatmentenclosure can have a vertical or horizontal orientation, with the beamassembly being on a support (e.g. a bar) along the long axis. The barcan then rotate around the patient, for example, to provide acylindrical coverage of the patient. The beam assembly can also haveangular degrees of freedom, e.g., the beam can be tilted up and down andside to side. Multiple beam assemblies could be provided on a samesupport, and there can be multiple supports with different beamassemblies. A small treatment enclosure (particularly if it isself-contained with the beam assembly and detectors, such as cameras oran x-ray device) can facilitate calibration and provide greateraccuracy. In one embodiment, the treatment enclosure can also be used inpre-treatment scanning to create the mapping model, or to supplement apreviously obtained mapping model.

FIG. 2B shows a treatment enclosure 280 according to embodiments of thepresent invention. The enclosure 280 can be of any shape, and maysurround only part of the patient, e.g., just a torso. As shown, thepatient is standing, but the patient may be in any position (e.g.leaning against a central object, sitting, or lying down). When lyingdown, enclosure 280 can have the long axis horizontal. Enclosure 280 canbe made with a door for a patient to walk through, or in a horizontalmode a bed can slide in and out from one end of the enclosure. Beamassemblies 281 a and 281 b can be mounted to supports 282 a and 282 b.As shown, there are two supports, but other embodiments can have onesupport or more than two supports. The supports 282 can move on a track283, which can be near either or both ends of enclosure 280. As shown,track 283 is circular, thereby allowing the supports to rotate. The beamassemblies 281 can rotate, and are shown rotated from horizontal (up anddown) and rotated from vertical (left and right). In addition to thebeam assemblies, imaging devices 284 a and 284 b may be attached tosupports 282 or to other support structures, such as an inner wall ofenclosure 280. These imaging devices can include, for example, x-raymachines, optical cameras (e.g., in the visible and/or infraredspectrum), radio frequency receivers to receive signals from activesensors, or any other suitable imaging device.

III. Using Multiple Scans of Same Patient

As mentioned above, the patient can be scanned prior to treatment, inorder to determine a function that maps marker positions to the locationof certain tissue (diseased and/or healthy). In some embodiments,multiple scans of the same patient can be used to determine the mappingfunction.

In one embodiment, each scan corresponds to a different physicalposition of the body. Each scan can provide the positions of the markersand of the particular tissue (e.g. a tumor). Thus, for N markers, eachscan can provide 3*(N+1) values, which can be considered as amulti-dimensional data point. An analogy to a simple two dimensionaldata point (X,Y) as determined by a function Y=f(x) is that thecoordinates of the markers are the input values X, and the location ofthe tumor is the output Y.

In some embodiments, coordinates of the markers can be measured from anyorigin, e.g. a marker can be considered the origin. Thus, 3*N values maydefine the multi-dimensional data point in the relative coordinatesystem, with 3*(N−1) values for the non-origin markers and 3 for thetumor location relative to the origin marker. In one embodiment, certainmarkers may be discarded if the dependence of the tumor location on themarker position is flat. For example, a change in the marker positionwould not affect the tumor location. In this manner, the best or mostinformative markers can be used.

In one embodiment, the data points can be interpolated, curve fit, etc.to determine a surface that maps the relative coordinates of the markers(e.g. relative to the origin marker) to the relative coordinate of thetumor. In various embodiments, the relative coordinate of the tumor canbe defined as a center of mass, center of volume, or other average valuerelated to the tumor.

In another embodiment, the shape of the tumor, as determined fromscan(s), can be superimposed onto the tumor coordinate. In oneimplementation, changes in the shape or orientation of the tumor canalso be determined as outputs to a functional model. For example, theorientation can be computed as a separate mapping, as defined by one ormore parameters, such as the three Euler angles.

IV. Using One Scan of Current Patient

In some embodiments, only one scan (or just a few) of the patient may berequired. The problem becomes how to obtain changes in the tumorlocation with changes in physical position when only one physicalposition is obtained. In one embodiment, specific scans or scaninformation from other patients are used. The functional model can thenbe obtained using a combination of the one scan for the specific patientand the scan information from one or more other patients, e.g., havingsimilar body shape, height, width and/or body mass. In this manner, thenumber of scans for a particular patient can be reduced, and the scansfrom other patients can be re-used, thereby reducing a total number ofscans needed, and potentially providing even greater accuracy.

In one embodiment, markers are placed at the same locations for controlpatient(s). In one embodiment, the location are defined with respect tocertain body parts, e.g., at belly button, between eyes, top of spinalcord, etc. As an alternative, body parts that have a fixed relationshipto each other can have substituted for placement of a marker. Themarkers can then be placed at a same location on the current patient.

In one embodiment, the control patients are the same body type as acurrent patient. For example, control patients of various types may beused, with information from patients with a similar body type being usedin combination with the scan of the current patient.

In one implementation, a mapping function can be determined for eachcontrol patient. The mappings for different control patients can then beaveraged together. In another implementation, the various scans candefine data points across patients, and a single mapping can bedetermined. In one aspect, the data points can be grouped by the patientand then scaled prior to combining to form the single mapping.Accordingly, in one embodiment, a general mapping function is determinedfrom the control patients. If body type (e.g., male, female, overweight,athletic, pear-shaped, muscular, etc.) is accounted for, embodiments canhave different mapping function for each body type. Control groups canalso be organized by the location of tumor, e.g., which organ has thetumor.

The single scan for the current patient can be used as a scalar on themapping from the control group. For example, the size of the mappedsurface (i.e. a surface that defines the tumor location in themulti-dimensional space for the data points of the scans) can beincreased or decreased a certain percentage based on the scalar. Thus,if a person has a same body type but is smaller or larger, a scalar canbe used. Different dimensions can have different scalars, e.g., adifferent scalar for X, Y, and Z, or a different one for R, theta, andphi for spherical coordinates.

Thus, a shape of the surface can also be modified. Other transformationsbesides a simple scalar can also be used.

In one embodiment, a reference object (e.g., reference object 370) canbe used in determining how the mapping function from the control groupis to be scaled. For example, the reference object can be used to scalethe relative coordinates from the one scan, thereby altering the scalarfor the control mapping determined from the scan. As another example,the reference object's position relative to features of the patient'sbody (e.g., eyes, shoulder, etc) can be used, at least partly, todetermine the scaling function to be applies to the control mapping.

In another embodiment, multiple scans can be used to determine how ageneral mapping function (e.g. as determined from one or more controlpatients) should be modified for the particular patient. In anotherembodiment, the few scans can be used to determine a first model, whichthen is modified based on the more general mapping function, e.g.,higher frequency changes of the multi-dimensional surface can beobtained from the general mapping function as more scans may have beenused to determine it.

V. Positioning of Beam Assembly

Embodiments can also be used to position the radiation beam. In variousaspects, the positioning can be accurate and the beam assembly can berelatively inexpensive compared to current beam assemblies. In oneembodiment, such positioning can be achieved using sensors on the beamassembly.

FIG. 7 shows a system for positioning a trajectory of a radiation beamfrom a beam assembly 720 according to embodiments of the presentinvention. Sensors 730 are placed on the beam assembly 720. Thepositions of the sensors can provide a location and an angularorientation of the beam assembly, thereby providing a trajectory of theradiation beam. The detectors can be connected with a computer systemthat determines the trajectory.

In one embodiment, the sensors are wireless (e.g. optical) and detectors740 can be used to determine the positions, e.g., by receiving radiationtransmitted from or reflected off of the sensors. The sensors canfunction in a similar manner to any of the embodiments described for thesensors on the patient.

In one embodiment, the system can be calibrated by knowing the exactplacement of the sensors on the beam assembly. With such knowledge, theposition of the sensors can have a static relationship to the trajectoryof the beam. For example, the beam assembly can be built with a certaintolerance that the trajectory will essentially be the same relative tothe position and orientation of the beam assembly (which is known fromthe sensors). The location of the sensors in the room can be calibratedin a separate step, which may be the same step as the calibration forthe sensors on the patient.

In another embodiment, the system can be calibrated by detecting thepositions of the sensors and then detecting a trajectory of theradiation beam. In one aspect, the beam can be detected at two points todetermine the trajectory. The beam can be measured at a particular pointin a variety of ways, such as with detectors that are situated on theother side of the patient from the beam assembly. The detectors can havean array of elements having a known position, where the radiation beamactivates an element. Some radiation may be absorbed by an element, butsome radiation will pass through to activate an element of anotherdetector.

The beam assembly can then be moved to a variety of positions, and themeasurement performed again. Each set of sensor positions can define atrajectory, with these values defining a data point for the position ofthe beam assembly. Therefore, a functional relationship between sensorposition and trajectory can be obtained. Not every possible sensorposition need to be explored as a functional approximation can provideintermediate values. Also, changes in the trajectory for rotations (e.g.around a single axis) for a particular location can be assumed toprovide similar changes in trajectory for the same rotations at adifferent location of the sensors.

In yet another embodiment, detectors could be used to track theradiation beam during treatment to provide another layer of feedbackinformation. In one aspect, such tracking can happen at a coarser levelof refinement, such that the trajectory of the beam is determined by thesensors on the beam assembly more often, but the function of the sensorposition to trajectory is updated based on the detection of the beam atlarger intervals.

A beam assembly 720 that uses any one or more of the feedback mechanismscan be made cheaper (e.g. expensive stepper motor may not be required)and/or lighter. With these advancements, or even otherwise, two beamassemblies can be used to provide treatment within a shorter period oftime. In one aspect, using two or more beams can help to provide aquicker reduction of the diseased tissue than even half the timerequired for one beam. For example, the amount of heating of the tumorcan be greater than double with two beams than just one beam. In anotherembodiment, each beam can be lower power when used in combination.

In one embodiment, when a location of the tumor is known, a computer candetermine a particular trajectory of the radiation beam (e.g., as partof a particular path over time). The beam assembly can be moved and whenthe desired trajectory is achieved, the radiation beam can be turned on.

VI. Movement of Patient

As mentioned above, embodiments can sample the locations of the sensorson the patient at various intervals during treatment. If the patientmoves between samples, then the radiation beam may become unfocused fromthe tumor (or other diseased tissue), or hit vital healthy tissue. Thesystem can sample the sensor locations quite often in order to minimizesuch an error. However, if the motion is fast (e.g. relative to thesampling frequency of sensor location) and over a relatively largedistance (e.g. as compared to the size of the tumor) so that the motionis not a simple vibration, then errors can persist.

Some embodiments can account for patient movement during treatment,including movement that is relatively fast. Various responses to themovement can depend on the type of movement and can depend on theequipment and functional response of the beam assembly (e.g., the speedat which a beam assembly is positioned). The embodiments described belowcan be used with embodiments described above (e.g. using relativecoordinates) as well as other techniques, e.g., where sensors areattached to the tumor itself.

A. Predicting Location Between Samples of Position

FIG. 8A shows an intermittent error in beam positioning due to movementof a tumor 810 according to embodiments of the present invention. Attime 0 seconds, the beam 820 is focused on the tumor 810. The samplingfrequency of the sensors is 0.2 seconds. At time 0.1 s, the tumor 810has moved (e.g., due to the patient moving), and the beam 820 is nolonger focused on the tumor. If the movement was small, then the beammight simply be focused on an edge of the tumor. But, as shown, themovement was relatively fast, and thus the beam is no longer focused onthe tumor. Such an example may be an extreme example, but is used tobetter illustrate embodiments of the present invention.

At time 0.2 s, the beam 820 is again focused on the tumor 810. Giventhat the sensors were sampled at 0.2 s, the location of the tumor wasdeduced, for example, from the relative coordinates of the sensors usinga model as described in any of the embodiments described herein. Thisexample assumes that the beam was focused instantly when the sensorlocations were read; however, a delay can occur, which embodiments canalso account for, as is described below. At time 0.3 s, the tumor 810again has moved (e.g., with approximately a constant velocity oracceleration), and thus the beam 820 is again not focused on the tumor810.

Some embodiments can identify that motion is occurring, and useinformation about the motion to focus the beam in between sampling ofsensor locations (and thus between times when the tumor location isknown). Such embodiments can predict where the tumor will be, and thuspredict a particular trajectory of the tumor between samples. Forexample, the position of the tumor at several times can be used tocalculate an acceleration and or time. Thus, for linear motion, theequation (position (x)=0.5*acceleration*time²+velocity*time+initialposition) can be used to predict where the tumor location will be at anytime between the sampling times. In one embodiment, the values ofacceleration, velocity and initial position can be considered threedimensional vector parameters for the equation of motion. The variablesof acceleration and velocity can be computed using simple algorithms(e.g. using two data points for velocity or three for acceleration), ormany data points, which can involve optimization of a cost function.Other functional forms for law of motion can include simple harmonicmotion, which may be linear or circular.

Besides models that are based on laws of motion, time-dependentfunctions for predicting the a next location of the tissue at a futuretime period can have other functional forms. For example, Fourierfunctions (such as sine and cosine) Legendre polynomials, sphericalharmonics, or any other basis functions can be used to approximate thedata points obtained from measuring the location of the tissue overtime. The variables (e.g. linear coefficients) can be determined via anoptimization algorithm that minimizes a cost function, e.g., adifference in the time-dependent function and the measured locations ofthe tissue, a difference in the variables from one optimization (e.g. ata first time) to another optimization (e.g., at a later time). The othertime-dependent functions can be implemented in a same way as the laws ofmotion to determine where the beam should be pointing between samples orat a point in time that is later than the time of a positionmeasurement. As each new data point of the measured location of thetissue is obtained, the time-dependent functions can be updated througha new optimization of the cost function, which has changed due to thenew data point.

FIG. 8B shows an example of a prediction of a location of a tumorbetween sampling times according to embodiments of the presentinvention. In FIG. 8B, the location of the beam 830 is the same as beam820 at times 0 seconds, 0.1 seconds and 0.2 seconds. Given that there isdata for three sampling times, a velocity and acceleration of the tumorcan be determined. This acceleration and velocity can be used to movethe beam 830 to be focused on the tumor 810 at time 0.3 seconds, andpotentially any time between 0.2 seconds and 0.3 seconds. Thus, theupdate of the beam position can be more often than the samplingfrequency of the sensors.

In one embodiment, a minimum number of sampling locations can berequired before the movement of the tumor is predicted, and thepredicted location used to position the beam between sampling times.Such a requirement can ensure that the equations of motion are accurateand that the motion of the patient is consistent enough to determine apredictive equation.

In other embodiments, other equations of motion can be used. Forexample, circular motion could be detected, or other curvilinear motion.In one embodiment, a computer system can have a predetermined number ofequations for various types of motion. Each type of motion can beassociated with a particular equation. Once the location information ismatched to a type of motion (e.g. linear, curvilinear, rotational) thenthe corresponding equation can be chosen and parameters of thecorresponding equation chosen. Other types of motion, such as periodic,can provide combinations to determined the equation. For example, aparticular equations can exist for periodic linear motion and adifferent equation for regular linear motion. In some implementations,one type of motion can be initially identified, and subsequently, a newtype of motion can be identified (e.g., linear first and then periodiclinear subsequently). In one implementation, the parameters for eachpossible equation can be calculated at each sampling time, and a type ofmotion can be determined for that sampling time, with the correspondingequation being used to predict the tumor location until the nextsampling time. In another implementation, the decision of which type ofmotion and which equations of motion to use can be performed at everyNth sampling time, where N is greater than one. The determination forwhich equation (model) is to be used can be determined by comparing abest fit of the parameters of each model to the location information andselecting the model that provides the best fit. The best fit can bedetermined by calculating an error for each model, e.g., an errorbetween the model and the determined locations of the diseased tissue.

B. Predicting Location with Delay in Beam Positioning

A delay can exist between the time that the locations of the sensors aredetermined and the time that a new tumor location is determined fromcoordinates of the sensors (e.g. relative coordinates of the sensors).There can also be a delay between the time that the new tumor locationis provided to a beam positioning mechanism and the time that the beamis positioned at the input tumor location. After these delays, the tumormay have already moved to a new location. For example, in FIG. 8A or 8Bafter a sampling at 0.2 s, the beam may not be re-positioned until 0.21seconds, and thus the tumor would have moved to a new location based ona particular acceleration and velocity during the intervening 0.01seconds. In addition, any healthy tissue, which is sought to be avoided,can also have moved thereby causing the beam to hit vital healthy tissue(e.g. the heart). Such a problem could be even worse if these delays aregreater than the sampling frequency.

Some embodiments can reduce a beam positioning error due to a time lagbetween the time a set of position samples are electronically measuredand the time required to (i) record the measurements, (ii) process themeasurements, (iii) use the measurements to calculate where the beamshould ideally point, and then (iv) cause the beam assembly to move tothe new pointing position. By knowing how long the lag is for thissequence of measurement, processing and positioning steps, the beam canbe positioned according to an estimate of where the beam should ideallybe positioned at the end of the time lag, rather than using an estimateof where the beam would have been ideally positioned at the time themeasurements were sampled (at the beginning of the time lag) under theassumption the tumor (and possibly the undesired tissue) are stationary.

FIG. 9A shows a constant error in beam positioning due to movement of atumor 910 according to embodiments of the present invention. In thisexample, the sampling frequency is 0.1 seconds, but there is a delay of0.1 seconds from the time the sensor location are detected and there-positioning of the beam. At time 0.0, the beam 920 is focused on thetumor 910 (e.g. because the tumor 910 has been stationary). From time0.0 to 0.1 seconds, the tumor 910 moves and the new sensor locations aredetected. However, the beam 920 has not been re-positioned yet, so thereis an error.

At time 0.2 seconds, the beam is updated to have the position of wherethe tumor 910 was at time 0.1 s, but now the tumor 910 has moved to anew position. Thus, there is still an error. Accordingly, thepositioning may always lag behind the actual tumor location if the tumorcontinues to move faster than the system can re-position.

FIG. 9B shows an example of a prediction of a location of a tumorbetween sampling times where the prediction accounts for a delay betweensampling and positioning of a beam according to embodiments of thepresent invention. At time 0.1, the beam 920 is still not focused on thetumor 910 since the system is still processing the new positioninformation. However, for time 0.2 seconds, the system can use theposition information at time 0.1 seconds to predict where the tumor willbe at 0.2 seconds since that is the time the system knows corresponds tothe delay. For example, once the system receives the sampled locationinformation about the sensors at time 0.1 s, the system can calculatethe predicted tumor location at 0.2 s (and just skip over anycalculation of the tumor location at time 0.1 s since the beam cannot bepositioned quick enough anyway). The delay for a particular system canbe determined during a calibration process.

For time 0.3 s, the system can use the position information from times0.1 s and 0.2 s, to calculate the tumor location at time 0.3 s. In oneaspect, the tumor location of at time 0.3 s is fed into the beampositioning system prior to the time of 0.3 s. For example, assume thatthe delay to calculate the tumor location is 0.02 seconds and the delayto re-position the beam is 0.08 seconds, then the new tumor location iscomputed for 0.3 seconds (using the position information at times 0.1 sand 0.2 s) and is provided to the beam positioning system at 2.2seconds, so that when the beam is re-positioned at time 0.3 seconds, thebeam will approximately be at where the tumor is actually located at 0.3seconds.

In some embodiments, a delta A (e.g. 0.1 s) can be added to the time ofthe prediction equation so that the position that is fed into thepositioning system to position the beam is always 0.1 seconds greaterthan when the sensor locations were last sampled. For example, avelocity can be determined from the position of the tumor 910 at time0.1 s and the location at time 0.0. Then assuming linear motion, thisvelocity can then be used in equation (position(x)=velocity*(time+Δ)+initial position). Once further data points areobtained, more complex equations can be used with the time offset. Thus,in one embodiment, the computing system does not use the current time inthe equations of motion, but uses the current time plus a time offset byΔ.

Embodiments for handling the various delays can be combined. Thus, thebeam's position can be updated more often than the sampling points,based on equations of motion derived from recent location measurements(i.e. sensor locations and subsequent calculation of tumor location).And, the equations of motion for the updates can use a time offset sothat the position is the expected tumor location at the end of there-positioning process. For example, if sampling of sensor locations isdone every 0.2 s, a prediction engine can receive a new tumor locationevery 0.2 s; but the tumor location can be old by 0.02 seconds under theabove example, where the delay of calculating the tumor location fromsensor locations is 0.02. The prediction engine can predict the tumorlocation at a time of a current time 0.22 s (i.e. 0.02 seconds after thesampling time, in this example) plus a time offset of 0.08 s (the delayin the positioning mechanism) to obtain a predicted tumor location at0.3 s. Assuming the prediction engine computes a predicted tumorlocation every 0.1 s (which can be more often than the sampled sensorlocations are received), the prediction engine would compute the nexttumor location (e.g. using the same equations of motion used for thecalculation at time 0.22 s) at time 0.32 s with an offset of 0.08 s toprovide a predicted tumor location at 0.4 s.

Besides using a fixed offset A for computing the next position, thetime-dependent functions to predict tumor position (and possiblyundesired tissue position) can be used in combination with the responseof the beam assembly positioning as a function of time. The responsetime to position the beam may change over time, e.g., the response timemay be longer when the tissue is moving faster and the beam assemblymust move faster to keep up, thereby resulting in more time lag. Asanother example, different delays can be encountered depending on thelast position of the beam and what the new commands are. Such differentdelays can be due to different total distances that the beam assemblyneeds to be moved. The beam assembly can be made to move faster when thedistance to be moved is more, but in general, the movement speed of thebeam assembly should correlate to the time step for the new position(i.e. related to the average velocity of the tissue and/or beam over thetime period) so that the beam assembly would be focused on the tissueduring the movement of the beam.

The response time can be measured for each new set of input commands forchanging the position of the radiation beam, thus a function G of theresponse time that approximates these data points can be determined(e.g., by computing coefficients of basis functions that minimize a costfunction). The cost function can include contributions from a differencein the time-response function G from the measured response time. Thefunction G could also be determined from the values of thetime-dependent function for the tissue. For example, the response timecould be estimated from the acceleration of the tissue, or from higherorder terms (such as the change in the acceleration). The time-responsefunction G can be pre-computed during a calibration process, and may beupdated during treatment.

C. Determining Beam Position from Tissue Position

Using the embodiments described above, one can calculate the position oftumor and/or healthy tissue at a given time. A number of differentpositions can be obtained for each time. The different positions caninclude multiple positions on a surface of a tumor, positions ofmultiple tumors, and multiple positions for various healthy tissues. Allof these positions can be used to determine an optimal beam position, aswell as any other beam properties, such as beam intensity, beam width,etc.

The optimal beam can be computed by optimizing a cost function. Forexample, the optimal position can minimize the cost function, which canhave contributions due to tumor and healthy tissue. The cost functioncan decrease when there is more overlap of the beam with the tumor (i.e.the beam is hitting the tumor), but increase if there is more overlapwith healthy tissue (e.g. a penalty is paid for hitting healthy tissue).The cost function can be tailored such that the penalty for hittinghealthy tissue is high (and also may vary depending on the exact healthytissue that would be hit, such as the heart) relative to the benefit(i.e. reduction in the cost function) for more overlap for the diseasedtissue.

D. Method of Predicting Location of Tissue

FIG. 10 is a flowchart illustrating a method 1000 for tracking motion oftissue and determining an optimal beam position based on the motionaccording to embodiments of the present invention. Method 1000 usestime-dependent functions to predict motion of tissue and an estimate ofdelay in the system to account for various system errors.

In step 1010, one or more locations of tumor and/or healthy tissue aremeasured at a plurality of times. The measurement may be made asdescribed above, for example, combining a less precise method duringtreatment (e.g., using locations of fiducials) with a model for mappingthe less precise measurements to more precise measurements. The lessprecise measurement could be internal measurement, e.g., using standardx-ray, or external measurement, e.g., using wireless sensors (asdescribed above) or imaging techniques. The times may be the N mostrecent measurements, or all of the measurements within a prescribedtime.

In step 1020, time-dependent function(s) for motion of the tissues arecalculated based on the measured locations. Each different tissue canhave its own time-dependent function, and even multiple locations oneach tissue can have a separate time-dependent function. Each locationcan be broken up into separate dimensions (e.g. Cartesian coordinates,spherical, cylindrical, and so on), and thus each location have threedifferent time-dependent functions, one for each dimension.

The time-dependent function can be determined in a variety of ways. Forexample, one may use a set of basis functions (e.g. polynomials in timet), and determine the coefficients that best approximate the motiondefined by the measurements from step 1010. Thus, the time-dependentfunctions could be of the form a+bt+ct², with a being the initialoffset, b being velocity, and c being an acceleration term (e.g.proportional to acceleration). Higher order polynomials can be used, aswell as other basis functions. The coefficients of the basis functionscan be determined by optimizing a cost function, e.g., mean squaredifference or worst square difference between the time-dependentfunction and the measured data points from step 1010. Non-linearvariables can also exist within the basis functions, but such inclusioncan make their calculation more difficult.

Accordingly, a time-dependent function can have the generic form ofX_(I,J)(t)=F(C,t), where X is a matrix with one dimension (e.g. I) beingthree and the other dimension (e.g. J) being the number of locationswhose motion is being modeled, and where C_(I,J,M) is a 3^(rd)-ranktensor (or simply an array with three dimensions) of the coefficientsthat are determined via the optimization step. The index M can run overthe number of variables defining the time-dependent function for theparticular coordinate I of location J of a tissue. Then, C can bedetermined by optimizing a cost function E(C,t,Y), where Y is themeasured data points from step 1010.

In one embodiment, E can equal Σ(Y−F)², where the sum is over each timepoint, number of coordinates, and number of locations of tissue beingtracked. Note that each time-dependent function can be treated as aseparate function. Alternatively, the motion for different coordinateslocations can be dependent on each other, e.g., the locations on asurface of a tumor would have some correlation with each other.Additionally, the variables for the location(s) of different tissue canbe calculated with different accuracy. This may be achieved usingdifferent weightings in the cost function E. For example, the sum of theleast square errors for a particular location(s) of an object (tissue)can be multiplied by a larger factor in order to give more importance toobtaining accurate values of C for the object.

The function F can be re-calculated for each new data point, or everyNth data point, where N is grater than one. The calculation of F can beindependent from how often a new command is given to the apparatus forpositioning the beam. For example, F can be re-calculated every 0.5seconds, but a new command can be sent to the beam positioner (alsocalled a movement mechanism) every 0.1 seconds. Thus, the last F can bere-used to determine new positions for the beam.

In step 1030, the delay Δt for positioning the beam is estimated. In oneembodiment, Δt could be chosen as a fixed value. For example, the systemcould assume that from the time of computing the locations (which couldinclude or not include determining the time-dependent function F),including the time to compute the optimal beam position, until the beamis positioned at its new designated position (i.e. as designated by thecommands given to the positioner) is a constant. In another embodiment,the value of Δt can be different. For example, if the tissue is movingfaster, it will take longer for the beam assembly to move into thecorrect position. Thus, Δt can be larger. Note that if Δt was largeenough, the beam may not reach its final designated location by the timea new command is given to the positioner.

For a variable delay Δt, the time may be estimated based on the valuesof C. For example, the maximum coefficient for the velocity oracceleration can be used to determine Δt, as that acceleration candictate how long it will take to position the beam. In yet anotherembodiment, the value of Δt can vary for each location being tracked.

In other embodiments, Δt can be determined from any combination ofdistance traveled for last time step, error of predicted position fromactual position of tissue, and a beam error of actual trajectory of thebeam from an optimal trajectory of the beam. Using the feedback of thebeam error can allow for machine learning, e.g., via optimizationalgorithms to determine better input commands into a beam assembly formoving the beam. The actual trajectory can be computed, e.g., asdescribed in section V above.

In step 1040, the position of each location J of tissue is determined attime t+Δt_(J), where t is the current time. The result is that thelocation of the tissue is computed for a future time. Since the beam isexpected to take Δt_(J) to move to the position at t+Δt_(J), the beam isexpected to move along a similar trajectory that that the tissue ismoving. Thus, the error is reduced compared to using the position of thetissue at the current time.

In step 1050, the optimal beam position can be determined, e.g., asdescribed above. For instance, a cost function that uses locations oftumor tissue and healthy tissue can be used to find a beam trajectorythat reduces risk to vital organs while providing radiation to thetumor. In some cases, the radiation beam could be turned off if thecertain criteria cannot be met (e.g., the cost function is above acertain value, which can indicate that healthy organs would be damaged).Once an optimal beam position is determined commands for a beampositioner can be determined. In one aspect, the optimal beam positioncan be a command.

In step 1060, the command for the new position is sent to the beampositioner. The commands may be analog or digital signals. The beampositioner may be a stepping motor. In one embodiment, the commands maybe high level commands that specify a position of the beam assembly or aparticular trajectory. The beam positioner can include a processor thatreceives the high level position commands and determines the specificsignals to send to actuators for moving the radiation beam.

Regarding the calculation of the time-dependent functions, someembodiments can use certain information to determine what kind of motionis occurring. For example, which sensors are moving can be used topredict how the patient's body is moving. If the sensors on thepatient's torso are moving rotationally, then the person's whole body islikely turning. If the tumor is located within the torso, the motion ofthe tumor is likely around an axis within the patient's body. Thus,rotating motion can be assumed, and the corresponding equations can beused. Certain criteria can be used to classify the type of motion, andthen use equations corresponding to that type of motion. Otherimplementations can use a single more general equation for multipletypes of motion.

As another example, the sensors could identify that the patient ismoving his/her arm or leg. If the tumor is within the arm or leg, thenthe motion can be constrained by knowledge of the patient's body, suchas length of the arm or leg and knowledge that only certain types ofmotions are possible (e.g. hinge-like motion for the elbow or knee).Thus, the knowledge of the type of motion and the physical constraintsof what motions are possible can be used to accurately predict where atumor may be.

In one embodiment, the beam can be turned off if the movement of thepatient is measured (via the sensors on the patient) to be faster than athreshold value, and/or erratic enough that a prediction is deemed notto be accurate within a threshold. The threshold may be determined basedon how fast the system can determine the tumor location and change thebeam trajectory, e.g., a latency of the system. This threshold for therate of acceptable movement can be determined during a calibrationprocedure, e.g., using a dummy instead of a real patient.

VII. Determining Trajectory of Optimal Beam Position

In the last section, the position of the tumor as a function of time wasdetermined. Based on this predicted motion, an optimal beam position wasdetermined for a particular time. In the embodiments of this section,the position of the tumor as a function of time need not be determined.Instead, a beam trajectory can be determined as a time-dependentfunction of beam position (e.g., 3-dimensional location and 2-dimenionalangle). The beam trajectory can be determined to minimize an errorbetween an actual beam position (e.g., as measured) and an optimal beamposition at a set of times. The optimal beam position at a particularinstant in time can be determined based on a determination of a positionof a tumor and/or healthy tissue at the particular instant in time (e.g.via a measurement made at that instant in time).

A. Method Using Feedback Error

FIG. 11 is a flowchart illustrating a method 1100 for determining anoptimal beam trajectory based on feedback error according to embodimentsof the present invention. Method 1100 uses time-dependent functions thataccount for the motion of the tissue. The time-dependent functions couldbe used to predict the motion of the tissue, or used to predict thechange over time of an optimal beam trajectory or inputs to a beampositioner.

In step 1110, location(s) of tumor and/or healthy tissue is measured atan instant in time. The location can be performed using methodsdescribed herein, e.g., using a mapping model obtained frompre-treatment scans. The locations of the tissue could also be obtaineddirectly with fiducials attached to the tissue. Any suitable method formeasuring the location may be used.

In step 1120, an optimal beam trajectory can be determined based on thelocation(s) of the tissue(s) at the instant in time. For a givenlocation of tumor and/or healthy tissue, an optimal trajectory can bechosen. The term optimal as used herein does not require the besttrajectory possible, but a value that is determined optimal within aspecific criteria (e.g., a cost function is below a certain value).Accordingly, the optimal trajectory could hit some healthy tissue, butthe amount would be within specific parameters.

In step 1130, the actual beam trajectory is measured at the instant intime. The actual beam trajectory can be measured as described herein.For example, the radiation beam may hit detectors, which can identify aparticular location of the disturbance of the detectors. As anotherexample, beam sensors (e.g. as described in FIG. 7) can be used todetermine the beam trajectory at the particular instant in time.

In step 1140, an error between the actual beam trajectory and theoptimal beam trajectory at the instant in time can then be determined.The error can result from various factors as described herein. An errorcan be computed for each degree of freedom of the beam trajectory, e.g.,three spatial coordinates and two angular coordinates. The measurementsof the actual beam trajectory can be determined on a continuous basis,and stored with a time stamp.

Once the tumor location is determined, the time t₀ can also be stored,so that the corresponding optimal beam trajectory at time t₀ can becompared to the actual beam trajectory at time t₀. In one embodiment,the error for different degrees of freedom can be weighted differently,e.g., the angular degrees of freedom can be weighted higher as they mayhave more of an impact on the change of the cost function for a beamtrajectory.

In step 1150, a time-dependent function for obtaining beam trajectoryparameters is updated based on any one or more of the values obtained insteps 1110-1140. In various embodiments, the time-dependent functionscan specify the motion of the tissue, the change in the optimal beamtrajectory over time, and the change in input commands to a beampositioner. The update can include changing a time offset fordetermining the next input command (e.g. providing a command for afuture point in time to account for delays in the system) or parametersthat affect the actual next input command (which could be any parameterfor any of the time-dependent functions).

In step 1160, one or more commands are sent to a beam positioner. Theinput commands provided at a time t₀ could be for a different beamtrajectory than the optimal beam trajectory at time t₀. For example, theinput could be for a greater position than the optimal position at timet₀, but due to time lag Δ, the actual beam position at time t₀+Δ will beor approximately be the optimal beam position for time t₀+Δ. Thus, theinput commands can be determined to reduce the error between the actualand optimal beam trajectory for a set of measurements at differenttimes.

In some embodiments, the beam assembly can have a continuous motion asopposed to discrete movements to new positions. For example, commandscan provide parameters for equations of motion of the beam assembly, asdescribed herein. Such parameters can include velocity and acceleration,or other variables for any suitable time-dependent function. Thepositioning system can then move the beam assembly according to thoseequations, whose parameters are based on the positions of the sensors.Such embodiments can take in account present or past beam assemblyvelocity and/or present or past beam assembly acceleration. For example,changes in velocity or acceleration to new values can have differentdelay based on what the current or previous values were. A time-responsefunction G to predict delays in positioning the radiation beam can becomputed as described above. This time-response function can becalibrated and recorded. The time-response function G can then be usedto estimate the ideal beam position commands (e.g. by determining theproper time offset at a given instant in time) or simply changing thevariables to account for any delays.

B. Updating Time-Dependent Function for Beam Position Parameters

The feedback of the errors in the actual beam position and the optimalbeam position can be used to various ways to update the time-dependentfunctions of the beam position parameters. For example, a time-dependentfunction can be determined for the optimal beam trajectory, and the beamerror from step 1140 can be used to determine a time offset (e.g. due tolag), in a similar manner as explained for method 1000. As anotherexample, a time-dependent function can be determined for the beamposition parameters. This time-dependent function would typically not bethe same as for the optimal beam trajectory, and thus can incorporateany lag in the system into the function itself without using a timeoffset.

Update Δt

The time-dependent function for the optimal beam trajectory can becalculated from the optimal beam position determines at a plurality oftimes. Each position of the beam can have a separate time-dependentfunction. As the beam can have two angular degrees of freedom, alongwith the three-dimensional spatial coordinates, five time-dependentfunctions could be used. The functions can have an assumed functionalform (basis functions), such as polynomial, which could be of the forma+bt+ct² or of higher order, as mentioned above. But, other basisfunctions suitable for periodic motion can be used. A cost function,such as least square error, can be used to determine the variablesdefining the functions.

Even if the time-dependent function was of such precision to exactlypredict the next optimal beam position, there can still be an error dueto the imprecision of the positioning mechanism for the beam, or anytime lags in the calculations and the positioning. Thus, an beam errordetermined in step 1140 can be non-zero. To account for such errors, atime-offset Δt can be used in a similar manner as described above. Asingle time-offset Δt can be used for all of the time-dependentfunctions, or the time-offset Δt can vary between the differenttime-dependent functions. Thus, each degree of freedom can have its ownvalue for Δt. The value of Δt can be determined in a similar manner asmentioned above. For example, Δt can be determined from the beam error,the variables of the time-dependent functions (e.g. a coefficientcorresponding to velocity and/or acceleration), and the change inposition of the tissue being tracked between sampling times.

In one embodiment, the beam error can be used as feedback to increase ordecrease the value of Δt. For example, if the error is a result of anovershoot (i.e. the beam was moved past the optimal location), the valueof Δt can be reduced for the next determination of the beam position.The exact amount of reduction can be determined via an optimizationalgorithm that uses previous errors and the corresponding Δt values. Foran undershoot, the value of Δt can be increased. If the error is zero oralmost zero (e.g. within a threshold of zero), then the value of Δt canremain unchanged. As a beam error can be computed for each degree offreedom of the beam position, a different value of Δt and change to Δtcan be used for respective time-dependent functions corresponding to thedifferent degrees of freedom. As the value of Δt is being updated, thetime-dependent function of the optimal trajectory can be re-calculatedfor each new data point of the optimal beam position.

Update Coefficients for Time-Dependent Function of Beam Parameters

In another embodiment, a time-dependent function(s) of the optimaltrajectory is not calculated, but instead a time-dependent function(s)of the input positions (commands) into the beam assembly for positioningthe beam. In this manner, the time-dependent function is not necessarilyrelated to any particular movement, but can be computed as the functionthat minimizes the beam error. However, input values for one or moreprevious positions of the tissue may be used.

The initial values for the variables of the time-dependent functions canbe computed in a similar manner as for the optimal beam trajectory. Forexample, a function approximating the data points of the optimal beamtrajectory can be computed. In another embodiment, the error at aparticular instant in time can be paired with a particular input to thebeam assembly, thereby providing an error in the initial values for thevariables. Combining the error with the actual input values (e.g., inputposition), one can determine an estimated value for the optimal inputvalues. The time-dependent functions for the input positions can then becomputed in a similar manner as any of the functions mentioned above.One can also compute the time-dependent function as delta value for howthe beam assembly should move based on a most recent value for the beamposition. This delta value in the change of the function value canitself vary over time, e.g., as computed based on an optimization of acost function using previous errors.

Once the variables of the time-dependent function(s) for the inputpositions are determined, the variables themselves can be updated basedon the measured beam error. The variables can be updated in variousways. For example, the variables can be updated as each new error pointis received. The direction of change can be computed in a similar manneras for Δt. As another example, the variables can be updated by combiningthe error with the actual input values, as is described above.

In practice, the combining the error with the actual input values caninvolve optimization algorithms, such as conjugate gradient (with theerror being the gradient) or quasi-Newton methods, or other types ofmachine learning. The basis functions for the time-dependent functionscan include neural networks and delta functions (e.g., simply vectorvalues at different instances in time), as well as others mentionedabove. The new values for the variables defining the time-dependentfunction(s) would then be chosen so as to minimize (or at least reduce)the measured beam error. The cost function for the optimization wouldinvolve the beam error(s) (e.g., one for each degree of freedon), andcould simply be a sum of the beam errors at the times being used, orsome other function. The various beam errors could be given differentweightings, e.g., if reducing the error for angle is more important thana spatial placement of the beam assembly itself, or vice versa. The useof a value of Δt can also be combined with this method.

VIII. Transforming Pre-Treatment Image

As mentioned above, a digital pre-treatment body image of a patient canbe created using various techniques (such as CT, MRI, and ultrasound).The pre-treatment body image can include a characterization of thespatial characteristics of one or more first components (e.g. tumorand/or healthy tissue) of the body anatomy relative to one or moremarkers. As detailed above, the markers may be natural features of thepatient's body (e.g., particular locations on bones, a nose, bellybutton) or artificial markers that are added to the patient's body (e.g.on the surface or internally). The digital pre-treatment body image canbe created using one imaging technique (e.g. the markers also are imagedwith the same technique as the tumor tissue) and two techniques are used(e.g. the marker locations are determined with optical or radiofrequency signals).

The pre-treatment body image may not be consistent with a treatmentcoordinate system. An embodiment can determine whether the pre-treatmentbody image is consistent with the treatment coordinate system. If thepre-treatment body image is not consistent with a treatment coordinatesystem, the pre-treatment body image can be mapped into a correctedpre-treatment body image that has a coordinate system that is consistentwith the treatment coordinate system. The treatment coordinate systemcan enable the location and/or orientation of the one or more firstcomponents of the body anatomy with respect to a location and/or apointing angle of a radiation treatment apparatus (e.g. a beamtrajectory). For example, the pre-treatment body image can be scaled tothe resolution (e.g. by altering the number of pixels in the image) ofthe detectors used during treatment to detect the sensors and positionthe beam, thereby allowing a unified coordinate system. This mapping canbe performed before treatment begins. If the pre-treatment body image isconsistent with a treatment coordinate system, then no correction may benecessary, and the corrected pre-treatment body image would be thepre-treatment body image.

During treatment, a digital treatment body image can be created. Thedigital treatment body image is consistent with the treatment coordinatesystem. The sensors can be located as key features on the treatment bodyimage in the treatment coordinate system. The positions of the sensorscan be obtained in various ways (such as x-rays, MRI, optical imaging,or ultrasound). For example, an x-ray scan can provide an image withidentifiable locations of bones, fiducials, or other sensors thatprovide a signal to detectors. As another example, optical imaging usingvideo or still pictures (or other wireless communication) can be used todetect natural body features or artificially added sensors. Any of theseand other suitable techniques can provide a digital treatment bodyimage. In one embodiment, the pre-treatment body image is created in adifferent apparatus than where the treatment body image is created.

A best-fit process (e.g. using optimization techniques described herein)can be used to map information (e.g. positions of tissue and sensors)from the corrected pre-treatment body image to the treatment body imageto create an enhanced treatment body image, which is consistent with thetreatment coordinate system. The best-fit mapping can determine aposition offset and/or a rotation offset to apply to the entirecorrected pre-treatment body image, to respective sections of thecorrected pre-treatment body image (e.g., if the body has a twist or isbent), or different offsets for different components (e.g. sensors andtissue) to re-position the corrected pre-treatment body image. Forexample, the offsets can minimize a position difference as measured inthe treatment coordinate system between a set of one or more commonfeatures (e.g. sensors and tumor tissue) in the re-positioned correctedpre-treatment body image and the same set of common features in thetreatment body image. The optimization can be constrained so that theoffsets reflect possible distances between the two different features(e.g., a hip joint may only have a certain range of possible distancesfrom a nearby tumor).

The enhanced treatment body image can be used to identify a feature(e.g. a sensor) in the image and to determine a desired radiation target(e.g. the tumor tissue) within the treatment coordinate system.Identified features can also be used to determine undesirable radiationtargets (e.g. healthy tissue) that is not to be radiated. A controlprocessor can determine a pointing location and/or pointing angle of theradiation treatment apparatus (e.g. a beam assembly) that will cause aradiation beam to hit the desired radiation target (e.g. beam has anoptimal beam trajectory). Commands can be provided to the radiationtreatment apparatus to cause the radiation treatment apparatus to moveto the pointing location and/or pointing angle and deliver a radiationdose.

The treatment body image may be body points identified by markers(sensors) placed on the body. In one embodiment, the markers can belocated with wireless position finding techniques, and the treatmentapparatus can include detectors to locate the markers in the treatmentcoordinate system. In another embodiment, the markers can be locatedwith video or still camera imaging techniques, and the treatmentapparatus can includes video or still cameras to locate the markers inthe treatment coordinate system. The placement of the markers on thebody during treatment imaging can be the same, or within a tolerance, asthe placement of pre-treatment markers placed on the body duringpre-treatment imaging. The treatment markers and the pre-treatmentmarkers can have the same image properties for pre-treatment imaging andtreatment imaging. The markers can have an image property that providesenhanced marker location during treatment imaging (e.g. relative toother features in the image) and the pre-treatment markers have an imageproperty that provides enhanced marker location during pre-treatmentimaging. In yet another embodiment, the markers (e.g. internal markers)can be located with x-ray techniques, and the treatment apparatus caninclude x-ray apparatus to locate the markers in the treatmentcoordinate system.

The treatment apparatus used to locate the markers in the treatmentcoordinate system can be calibrated prior to treatment by capturing anestimated position in the treatment coordinate system of a test markerof known location in the treatment coordinate system and applying acorrection factor to the estimated position so that it correctly maps tothe known position in the treatment coordinate system.

In one embodiment, the enhanced treatment body image is used to: (i)enhance one or more image properties and/or one or more locationestimates of a first set of image features (e.g. healthy tissue) in thetreatment image, or (ii) add one or more image features (e.g. thediseased tissue) in a second set of image features to the treatment bodyimage, where the second set of image features are identifiable in there-positioned corrected pre-treatment image and are not identifiable inthe treatment image. In one embodiment, the one or more first set offeatures can include image features resulting from markers placed on ornear the body. In another embodiment, the one or more first set offeatures can include body features or anatomy elements identified by abody anatomy identification algorithm applied to the image. The one ormore second set of features can include body features or anatomyelements.

Mapping the pre-treatment body image the treatment coordinate system canbe accomplished by calibrating the apparatus used to create thepre-treatment body image so that an absolute measure of dimensions isobtainable from the pre-treatment body image information. The mapping toa corrected pre-treatment body image can then be based on the knownabsolute dimension information available in the pre-treatment bodyimage. Mapping the pre-treatment body image into the treatmentcoordinate system can be accomplished by inserting calibration markersof known absolute geometry placed on or near the body during thepre-treatment imaging process. The known absolute geometry of thecalibration markers can be used to adjust the pre-treatment body imageso that the corrected geometry of the calibration markers in thecorrected pre-treatment body image is consistent with their knownabsolute geometries.

In one implementation, the location and/or a pointing angle of aradiation treatment apparatus can be determined by a pre-treatmentcalibration procedure wherein a position and/or pointing angle commandis provided to the radiation treatment apparatus. The resulting positionand/or pointing angle of a radiation beam can be measured with respectto the treatment coordinate system. The process may be repeated until acharacterization of multiple position and/or pointing angle commands andthe resulting position and/or pointing angle measured in the treatmentcoordinate system is sufficient to achieve the required accuracy duringtreatment. In another implementation, the location and/or a pointingangle of a radiation treatment apparatus can also determined by placingmarkers on the treatment apparatus elements that direct a radiationbeam, locating the position of the markers in the treatment coordinatesystem, and applying a mapping of the location of the markers in thecoordinate system to the location and/or a pointing angle of a radiationtreatment apparatus in the treatment coordinate system.

Determining a pointing location and/or pointing angle of the radiationtreatment apparatus that will cause a radiation beam to hit the desiredradiation target can include using one or more past positions of thedesired radiation target and an estimate of motion dynamics of thedesired radiation target to improve the accuracy of the pointinglocation and/or pointing angle with respect to the actual real timelocation of the desired radiation target, e.g., as described above. Theenhanced treatment body image may further utilized to identify one ormore undesired radiation features in the image and use the one or moreundesired image features to determine one or more undesired radiationtargets within the treatment coordinate system.

Determining a pointing location and/or pointing angle of the radiationtreatment apparatus may not only be based on the location of the desiredpresent state radiation target, but also based on the one or moreundesired present state radiation targets that are desired to be avoidedwhen determining the present pointing location and/or pointing angle ofthe radiation treatment apparatus in the treatment coordinate system.The desired treatment path can include a series of future pointinglocations and/or pointing angles that will result in more exposure tothe desired radiation target than is delivered to other body featuresincluding the one or more undesired radiation targets. As timeprogresses, each of the future pointing locations and/or pointing anglesmay be used to assist in deriving a present state pointing locationand/or pointing angle.

In one embodiment, the best-fit process to create an enhanced treatmentbody image can include identifying a first set of body model referencefeatures in the treatment image, determining a body-model orientationbased on the relative position of the body-model reference features inthe treatment body image, utilizing the body-model orientation to obtaina body-model enhanced version of the corrected pre-treatment body image,and then applying the position offset and a rotation offset to thebody-model enhanced corrected pre-treatment body image to create there-positioned corrected pre-treatment body image. The body model may bea mathematical model that determines an enhanced location estimate for asecond set of body features based on the relative position of thebody-model reference features in the treatment body image. The secondset of body features may be features that are not available, or havepoor quality or resolution in the treatment image.

In another embodiment, the best-fit process to create an enhancedtreatment body image can include identifying a first set of body modelreference features in the treatment image, identifying from a pluralityof secondary pre-treatment images a subset of two or more closest fitimages wherein the relative position of the body-model referencefeatures in the closest fit secondary pre-treatment images is close tothe relative position of the body-model reference features in thetreatment image, applying an interpolation algorithm to two or moresecondary pre-treatment images to create an improved interpolatedclosest fit pre-treatment body image, and then applying the positionoffset and a rotation offset to the improved interpolated closest fitcorrected pre-treatment body image to create the re-positioned correctedpre-treatment body image.

IX. Computer System

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 12in computer apparatus 1200. In some embodiments, a computer systemincludes a single computer apparatus, where the subsystems can be thecomponents of the computer apparatus. In other embodiments, a computersystem can include multiple computer apparatuses, each being asubsystem, with internal components.

The subsystems shown in FIG. 12 are interconnected via a system bus1275. Additional subsystems such as a printer 1274, keyboard 1278, fixeddisk 1279, monitor 1276, which is coupled to display adapter 1282, andothers are shown. Peripherals and input/output (I/O) devices, whichcouple to I/O controller 1271, can be connected to the computer systemby any number of means known in the art, such as serial port 1277. Forexample, serial port 1277 or external interface 1281 can be used toconnect computer system 1200 to a wide area network such as theInternet, a mouse input device, or a scanner. The interconnection viasystem bus 1275 allows the central processor 1273 to communicate witheach subsystem and to control the execution of instructions from systemmemory 1272 or the fixed disk 1279, as well as the exchange ofinformation between subsystems. The system memory 1272 and/or the fixeddisk 1279 may embody a computer readable medium. Any of the valuesmentioned herein can be output from one component to another componentand can be output to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 1281 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

It should be understood that any of the embodiments of the presentinvention can be implemented in the form of control logic using hardwareand/or using computer software in a modular or integrated manner. Basedon the disclosure and teachings provided herein, a person of ordinaryskill in the art will know and appreciate other ways and/or methods toimplement embodiments of the present invention using hardware and acombination of hardware and software.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C++ or Perl using, for example, conventional or object-orientedtechniques. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission, suitable media include random access memory (RAM), a readonly memory (ROM), a magnetic medium such as a hard-drive or a floppydisk, or an optical medium such as a compact disk (CD) or DVD (digitalversatile disk), flash memory, and the like. The computer readablemedium may be any combination of such storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium according to an embodiment of the presentinvention may be created using a data signal encoded with such programs.Computer readable media encoded with the program code may be packagedwith a compatible device or provided separately from other devices(e.g., via Internet download). Any such computer readable medium mayreside on or within a single computer program product (e.g. a harddrive, a CD, or an entire computer system), and may be present on orwithin different computer program products within a system or network. Acomputer system may include a monitor, printer, or other suitabledisplay for providing any of the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including a processor, which can beconfigured to perform the steps. Thus, embodiments can be directed tocomputer systems configured to perform the steps of any of the methodsdescribed herein, potentially with different components performing arespective steps or a respective group of steps. Although presented asnumbered steps, steps of methods herein can be performed at a same timeor in a different order. Additionally, portions of these steps may beused with portions of other steps from other methods. Also, all orportions of a step may be optional. Additionally, any of the steps ofany of the methods can be performed with modules, circuits, or othermeans for performing these steps.

The specific details of particular embodiments may be combined in anysuitable manner without departing from the spirit and scope ofembodiments of the invention. However, other embodiments of theinvention may be directed to specific embodiments relating to eachindividual aspect, or specific combinations of these individual aspects.

The above description of exemplary embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdescribed, and many modifications and variations are possible in lightof the teaching above. The embodiments were chosen and described inorder to best explain the principles of the invention and its practicalapplications to thereby enable others skilled in the art to best utilizethe invention in various embodiments and with various modifications asare suited to the particular use contemplated.

1. A method for providing radiation treatment to a patient, the methodcomprising: detecting a position of each of a plurality of sensors ofthe patient's body, the positions of the sensors being determined withrespect to a reference point having a known spatial relationship to aradiation beam assembly, the radiation beam assembly configured toprovide a radiation beam; determining relative positions of the sensorswith respect to each other; accessing a first mapping model that mapsthe relative positions of the sensors to determine a relative locationof diseased tissue of the patient, the relative location of the diseasedtissue being relative to the positions of the sensors; and using thepositions of the sensors and the relative location of the diseasedtissue to direct a radiation beam of the radiation beam assembly to thediseased tissue.
 2. The method of claim 1, wherein the radiations beamassembly operates in a treatment coordinate system, the method furthercomprising: obtaining a digital pre-treatment body image that includesthe sensors and the diseased tissue; mapping the pre-treatment bodyimage into a corrected pre-treatment body image having a coordinatesystem that is consistent with the treatment coordinate system; usingthe sensor positions to create a digital treatment body image in thetreatment coordinate system; creating an enhanced treatment body imageby: performing an optimization to map the sensors from the correctedpre-treatment body image onto the digital treatment body image, theoptimized mapping using a position offset and/or a rotation offset tominimize a position difference between a common feature in the correctedpre-treatment body image and the treatment body image.
 3. The method ofclaim 2, wherein the pre-treatment body image is created in a differentapparatus than where the treatment body image is created.
 4. The methodof claim 1, wherein the plurality of sensors includes at least threesensors, and wherein at least one of the sensors are internal to thepatient's body.
 5. The method of claim 1, wherein detecting a positionof a sensor at the surface of a patient's body includes triangulating asignal detected from the sensor using at least two sensor detectors. 6.The method of claim 1, further comprising: accessing a second mappingmodel that maps the relative positions of the sensors to a location ofhealthy tissue of the patient, the location of the healthy tissue beingnear the diseased tissue; and using the location of the healthy tissueto direct the radiation beam away from the healthy tissue.
 7. The methodof claim 1, further comprising determining the first mapping model by:taking scans of a first patient in a plurality of physical positions,wherein each physical position is different and involves a translationand/or a rotation of one or more selected from the first patient's head,torso, and appendages relative to another position, wherein the firstpatient has a plurality of first markers attached to the first patient'sbody; for each scan, determining relative positions of the first markerswith respect to each other and with respect to the diseased tissue; andusing the relative positions of the first markers at each of theplurality of positions to calculate the functional model, wherein thefunctional model provides an approximate location of the diseased tissuefor an input of relative positions of the first markers for new physicalpositions of the first patient.
 8. The method of claim 1, furthercomprising determining the first mapping model by: performing at leastone scan of the patient to detect a location of the diseased tissue ofthe patient; and correlating the location of the diseased tissue topositions of markers at a surface of the patient's body, wherein thepositions of the markers have a predetermined spatial relationship withthe positions of the sensors.
 9. The method of claim 8, furthercomprising: performing at least one additional scan of the patientduring the radiation treatment; identifying locations of the diseasedtissue in the scan and sensors in the at least one additional scan; andupdating the mapping model based on the identified locations in the atleast one additional scan.
 10. The method of claim 9, wherein thesensors in the at least one additional scan include at least oneinternal sensor that is internal to the patient's body.
 11. The methodof claim 8, further comprising: identifying, in an output of a scan, areference object of known size; and scaling the positions of the markersbased on at least one known length obtained from the reference object.12. The method of claim 8, wherein the first mapping model is determinedby using the correlation to modify a mapping model built from aplurality of scans of one or more other patients.
 13. The method ofclaim 8, wherein the sensors are placed at the same positions as themarkers.
 14. The method of claim 8, wherein the sensors are placed at aknown position relative to the positions of the markers.
 15. The methodof claim 8, wherein the at least one scan includes magnetic resonanceimaging (MRI) and/or computed tomography (CT).
 16. The method of claim8, wherein a plurality of scans are performed, and wherein the patientis in a different physical position for each of the plurality of scans.17. The method of claim 16, wherein a difference in physical position ofthe patient includes a rotation of the patient's body.
 18. The method ofclaim 16, wherein each scan provides a multi-dimensional data pointcomprising the location of the diseased tissue and the positions of themarkers, further comprising: calculating a function that approximatesthe functional behavior of the plurality of multi-dimensional datapoints, wherein the function provides the first mapping of the locationof the diseased tissue to positions of the sensors that do notcorrespond directly with the positions of the markers during theplurality of scans.
 19. The method of claim 18, wherein location of thetumor is defined by a position and one or more angles defining anorientation of a shape of the diseased tissue.
 20. The method of claim18, wherein the functional approximation is determined by a constrainedoptimization with constraints defined by a model of body movement. 21.The method of claim 20, wherein the constraints are dependent on a bodytype of the patient.
 22. The method of claim 1, further comprising:determining a body type of the patient from among a plurality ofpossible body types, wherein the first mapping model corresponds to thedetermined body type.
 23. The method of claim 1, further comprising:detecting a first position of each of a set of beam sensors at a firsttime, where a beam sensor is attached to a beam assembly that isconfigured to provide a radiation beam; determining a trajectory of theradiation beam from the first positions of the beam sensors; and usingthe determined trajectory at the first time to move the beam assemblysuch that the trajectory of the radiation beam is focused at a locationof diseased tissue of a patient.
 24. The method of claim 23, furthercomprising calibrating the beam assembly by: for each of a plurality ofpositions of the set of beam sensors, detecting a trajectory of theradiation beam; and based on the positions of the beam sensors and therespective trajectories, calculating a trajectory function thatapproximates the relationship between the positions of the beam sensorsand the trajectory of the beam assembly, wherein the trajectory functionis used to determine a trajectory of the radiation beam from the firstpositions of the beam sensors.
 25. The method of claim 23, furthercomprising: using the location of the diseased tissue to direct a secondradiation beam to the diseased tissue, wherein a trajectory of thesecond radiation beam is determined based on a second trajectoryfunction and beam sensors on a second beam assembly that provides thesecond radiation beam.
 26. The method of claim 1, further comprising:detecting a plurality of locations of the diseased tissue using at leastone sensor, each location being detected at a different time duringtreatment with the radiation beam; based on the plurality of locations,determining one or more parameters for a time-dependent motion modelthat accounts for a motion of the diseased tissue; using the motionmodel to determine a new trajectory for the radiation beam; providingthe new trajectory to a beam positioning system; and the beampositioning system adjusting the radiation beam to have the newtrajectory.
 27. The method of claim 1, further comprising: trackingmovement of the sensors; and shutting off the radiation beam when themovement is faster than a threshold value.
 28. The method of claim 27,wherein the threshold value is determined by how fast a trajectory ofthe radiation beam can be changed to account for the movement.
 29. Themethod of claim 1, wherein the sensors are wireless sensors.
 30. Themethod of claim 29, wherein the wireless sensors are optical sensors.31. The method of claim 1, further comprising: using the location of thediseased tissue to direct two or more radiation beams to the diseasedtissue.
 32. The method of claim 31, further comprising: using the two ormore radiation beams to provide lower power radiation beams as comparedto power required when using one radiation beam.
 33. The method of claim31, wherein the two or more radiation beams are configured to provideradiation to a larger surface area of the diseased tissue compared toone radiation beam.
 34. The method of claim 31, wherein the two or moreradiation beams are optimized to reduce damage to surrounding healthytissue while providing radiation to the diseased tissue.
 35. A computerproduct comprising a tangible computer readable medium storing aplurality of instructions for controlling a processor to perform anoperation, the instructions comprising: detecting a position of each ofa plurality of sensors of the patient's body, the positions of thesensors being determined with respect to a reference point having aknown spatial relationship to a radiation beam assembly, the radiationbeam assembly configured to provide a radiation beam; determiningrelative positions of the sensors with respect to each other; accessinga first mapping model that maps the relative positions of the sensors todetermine a relative location of diseased tissue of the patient, therelative location of the diseased tissue being relative to the positionsof the sensors; and using the positions of the sensors and the relativelocation of the diseased tissue to direct a radiation beam of theradiation beam assembly to the diseased tissue.
 36. A system forproviding radiation treatment to a patient, the system comprising: oneor more beam assemblies, each configured to emit a radiation beam; aplurality of detectors configured to receive signals from a plurality ofsensors of the patient's body; one or more processors that are incommunication with the one or more beam assemblies and the plurality ofdetectors and that are configured to: detect a position of each of thesensors using the signals received from the sensors, the positions ofthe sensors being determined with respect to a reference point having aknown spatial relationship to the one or more beam assemblies; determinerelative positions of the sensors with respect to each other; access afirst mapping model that maps the relative positions of the sensors todetermine a relative location of diseased tissue of the patient, therelative location of the diseased tissue being relative to the positionsof the sensors; and use the positions of the sensors and the relativelocation of the diseased tissue to direct a radiation beam of theradiation beam assembly to the diseased tissue. 37-81. (canceled)